Bibliographic Details
| Title: |
Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data. |
| Authors: |
Uddin, Shahadat1 (AUTHOR) shahadat.uddin@sydney.edu.au, Lu, Haohui1 (AUTHOR) |
| Superior Title: |
PLoS ONE. 4/18/2024, Vol. 19 Issue 4, p1-12. 12p. |
| Subject Terms: |
*MACHINE learning, *REGRESSION trees, *RANDOM forest algorithms, *SUPPORT vector machines, *DECISION trees |
| Abstract: |
Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article. [ABSTRACT FROM AUTHOR] |
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| Database: |
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